Durand NC, Shamim MS, Machol I, et al. Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments. Cell Syst. 2016;3(1):95-98. doi:10.1016/j.cels.2016.07.002
Durand NC, Robinson JT, Shamim MS, et al. Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom. Cell Syst. 2016;3(1):99-101. doi:10.1016/j.cels.2015.07.012
Rao SS, Huntley MH, Durand NC, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping [published correction appears in Cell. 2015 Jul 30;162(3):687-8]. Cell. 2014;159(7):1665-1680. doi:10.1016/j.cell.2014.11.021 PMID: 25497547; PMCID: PMC5635824.
Neva Durand, Ph.D.
Neva Cherniavsky Durand joined the Broad in 2021 as a research scientist in the Genome Regulation Observatory at the Broad Institute of MIT and Harvard, working under the direction of Noam Shoresh in collaboration with Brad Bernstein and Jason Buenrostro. She is broadly interested in using cutting edge technology to create robust analysis and visualization tools that facilitate fundamental discovery in gene regulation. Her current projects focus on analysis of single cell multiomics assays to elucidate cell-type specific function.
From 2012-2021, Durand was the chief computationalist in Erez Lieberman Aiden's group, where she created the Juicer pipeline for turning raw sequence reads into Hi-C maps that can be annotated and analyzed easily, and the Juicebox visualization software for easy exploration of complex data. This software, now the ENCODE standard and in use by hundreds of labs around the world, is a crucial tool for interrogating the 3D genome and led to important discoveries about how the genome folds to regulate cell function.
Durand earned her Ph.D. in computer science from the University of Washington in March 2009. In postdoctoral work with Andrew Zisserman at INRIA, Durand first tackled the problem of automatically finding facial attributes of humans in video to facilitate sociological research. Next, working under Tomaso Poggio at MIT CBCL, Durand helped build an object recognition system for aerial video that includes refinement of neuromorphic features, dictionary learning and feature selection, classification, and tracking.